As network communications among multiple computing devices have become ubiquitous, the quantity of information available via such network communications has increased exponentially. For example, the ubiquitous Internet and World Wide Web comprise information sourced by a vast array of entities throughout the world, including corporations, universities, individuals and the like. Such information is often marked, or “tagged”, in such a manner that it can be found, identified and indexed by services known as “search engines”. Even information that is not optimized for search engine indexing can still be located by services, associated with search engines, which seek out information available through network communications with other computing devices and enable a search engine to index such information for subsequent retrieval.
Due to the sheer volume of information available to computing devices through network communications with other computing devices, users increasingly turn to search engines to find the information they seek. Search engines typically enable users to search for any topic and receive, from this vast volume of information, identifications of specific content that is deemed to be responsive to, or associated with, the users' queries. To sort through the vast amounts of information that is available, and timely provide useful responses to users' queries, search engines employ a myriad of mechanisms to optimize the identification and retrieval of responsive and associated information.
Unfortunately, even with the aid of search engines, users are often overwhelmed by the sheer volume of information available. For example, typical topics searched for by users often result in millions of results that are identified by search engines. Additionally, certain types of search queries are often not adequately answered by the results returned by search engines. In particular, queries directed to human-based attributes, such as evaluations, opinions, experiences and the like often return search results that may not be what the user was searching for. For example, a query searching for experiences dealing with traffic situations in a particular city during a particular time of day may return search results directed to specific traffic incidents in the past, or a current, or historical, traffic data, but may not provide the sort of guidance a user would have been searching for with such a query.